Postgraduate Course: Prescriptive Analytics with Mathematical Programming (CMSE11431)
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||This course provides students with the fundamentals of linear and integer optimisation to model and analyse real-world business applications.
Optimisation problems are concerned with optimising an objective function subject to a set of constraints. When optimisation problems are translated in algebraic form, we refer to them as mathematical programs. Mathematical programming, as an area within Operational Research (OR), Management Science (MS) and Business Analytics (BA), is concerned with model building and strategies and methods for solving mathematical programs. In this course, we address model building in OR/MS/BA, present a variety of typical OR/MS/BA problems and their mathematical programming formulations, provide general tips on how to model managerial situations, and discuss solution strategies and present solution methods for linear and integer programs. The objective of this course is to enhance students' understanding of the critical nature of building appropriate mathematical models as simplified representations of realistic managerial situations, and the role such models play in prescribing solutions to decision making problems. The course also aims at training students to critically assess mathematical programming models and solution methodologies. In addition, students will learn how to use state-of-the-art prescriptive analytics tools in the context of decision problems faced by business managers. The course provides opportunities for students to learn from each other, from practitioners in the field, and from the latest theoretical and applied research in the field. The course will require students to work in groups on realistic projects in different business settings involving prescriptive analytics, and to present their work to the rest of the class and to an external panel when the projects are supplied by industry.
The course is organised around the following three main teaching blocks:
Block 1: Introduction to OR/MS/BA, typical methodological steps of an OR/MS/BA study, and model building with applications in business decision making.
Block 2: Linear programming (LP) - Review of basic concepts and methods; namely, the simplex method, sensitivity analysis, and duality theory with applications in business decision making.
Block 3: Integer programming (IP) -Basic concepts, relationship with linear programming, strategies and methods of solving integer programs; namely, brand-and-bound algorithms, cutting plane algorithms, and brand-and-cut algorithms, with applications in business decision making.
Student Learning Experience:
Students are expected to learn basic concepts and theories from lectures. In tutorial sessions, they will learn how to apply the basic concepts and theories learned in the lectures, as well as how to use optimisation solvers to address practical problems.
Tutorial/seminar hours represent the minimum total live hours - online or in-person - a student can expect to receive on this course. These hours may be delivered in tutorial/seminar, lecture, workshop or other interactive whole class or small group format. These live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2022/23, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Additional Information (Learning and Teaching)
Seminar/Tutorial hrs are the min total live hrs, online or in-person, students can expect to receive
|Assessment (Further Info)
|Additional Information (Assessment)
||60% coursework (individual) - assesses course Learning Outcomes 1, 2, 4
40% coursework (group) - assesses course Learning Outcomes 3, 4, 5
|No Exam Information
On completion of this course, the student will be able to:
- Discuss the concept and methods of prescriptive analytics, in general, and mathematical programming, in particular, using the proper terminology.
- Identify and properly state prescriptive analytics optimisation problems in different business settings, model them, choose the right solution methodology and methods and solve them using mathematical programming techniques
- Interpret solutions, formulate managerial guidelines and make recommendations.
- Critically discuss alternative prescriptive analytics approaches and methods.
- Communicate solutions effectively and efficiently to a critical audience of non-specialists.
|-H.P. Williams (2013). Model Building in Mathematical Programming, fifth edition, Wiley.|
-Bertsimas, D., & Tsitsiklis, J. N. (1997). Introduction to linear optimization. Belmont, MA: Athena Scientific.
-Chen, D. S., Batson, R. G., & Dang, Y. (2011). Applied integer programming: modeling and solution. John Wiley & Sons.
-S. P. Bradley, A. C. Hax, and T. L. Magnanti (1977). Applied Mathematical Programming, Addison-Wesley.
|Graduate Attributes and Skills
||Autonomy, Accountability and Working with Others
After completing this course, students should be able to:
Act with integrity, honesty and trust in all business stakeholder relationships, and apply ethical reasoning to effective decision making, problem solving and change management
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Apply creative, innovative, entrepreneurial, sustainable and responsible business solutions to address social, economic and environmental global challenges.
After completing this course, students should be able to
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to quality.
Knowledge and Understanding
After completing this course, students should be able to:
Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore and solve them responsibly.
|Course organiser||Dr Nader Azizi
Tel: (0131 6)51 1491
|Course secretary||Mr Sean Reddie
Tel: (0131 6)50 8074